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Abstract
Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth’s cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.
Abstract
Monitoring changes of precipitation phase from space is important for understanding the mass balance of Earth’s cryosphere in a changing climate. This paper examines a Bayesian nearest neighbor approach for prognostic detection of precipitation and its phase using passive microwave observations from the Global Precipitation Measurement (GPM) satellite. The method uses the weighted Euclidean distance metric to search through an a priori database populated with coincident GPM radiometer and radar observations as well as ancillary snow-cover data. The algorithm performance is evaluated using data from GPM official precipitation products, ground-based radars, and high-fidelity simulations from the Weather Research and Forecasting Model. Using the presented approach, we demonstrate that the hit probability of terrestrial precipitation detection can reach to 0.80, while the probability of false alarm remains below 0.11. The algorithm demonstrates higher skill in detecting snowfall than rainfall, on average by 10%. In particular, the probability of precipitation detection and its solid phase increases by 11% and 8%, over dry snow cover, when compared to other surface types. The main reason is found to be related to the ability of the algorithm in capturing the signal of increased liquid water content in snowy clouds over radiometrically cold snow-covered surfaces.
Abstract
The impact of assimilating Global Precipitation Measurement (GPM) Microwave Imager (GMI) clear-sky radiance on the track and intensity forecasts of two Atlantic hurricanes during the 2015 and 2016 hurricane seasons is assessed using the Hurricane Weather Research and Forecasting (HWRF) Model. The GMI clear-sky brightness temperature is assimilated using a Gridpoint Statistical Interpolation (GSI)-based hybrid ensemble–variational data assimilation system, which utilizes the Community Radiative Transfer Model (CRTM) as a forward operator for satellite sensors. A two-step bias correction approach, which combines a linear regression procedure and variational bias correction, is used to remove most of the systematic biases prior to data assimilation. Forecast results show that assimilating GMI clear-sky radiance has positive impacts on both track and intensity forecasts, with the extent depending on the phase of hurricane evolution. Forecast verifications against dropsonde soundings and reanalysis data show that assimilating GMI clear-sky radiance, when it does not overlap with overpasses of other microwave sounders, can improve forecasts of both thermodynamic (e.g., temperature and specific humidity) and dynamic variables (geopotential height and wind field), which in turn lead to better track forecasts and a more realistic hurricane inner-core structure. Even when other microwave sounders are present (e.g., AMSU-A, ATMS, MHS, etc.), the assimilation of GMI still reduces temperature forecast errors in the near-hurricane environment, which has a significant impact on the intensity forecast.
Abstract
The impact of assimilating Global Precipitation Measurement (GPM) Microwave Imager (GMI) clear-sky radiance on the track and intensity forecasts of two Atlantic hurricanes during the 2015 and 2016 hurricane seasons is assessed using the Hurricane Weather Research and Forecasting (HWRF) Model. The GMI clear-sky brightness temperature is assimilated using a Gridpoint Statistical Interpolation (GSI)-based hybrid ensemble–variational data assimilation system, which utilizes the Community Radiative Transfer Model (CRTM) as a forward operator for satellite sensors. A two-step bias correction approach, which combines a linear regression procedure and variational bias correction, is used to remove most of the systematic biases prior to data assimilation. Forecast results show that assimilating GMI clear-sky radiance has positive impacts on both track and intensity forecasts, with the extent depending on the phase of hurricane evolution. Forecast verifications against dropsonde soundings and reanalysis data show that assimilating GMI clear-sky radiance, when it does not overlap with overpasses of other microwave sounders, can improve forecasts of both thermodynamic (e.g., temperature and specific humidity) and dynamic variables (geopotential height and wind field), which in turn lead to better track forecasts and a more realistic hurricane inner-core structure. Even when other microwave sounders are present (e.g., AMSU-A, ATMS, MHS, etc.), the assimilation of GMI still reduces temperature forecast errors in the near-hurricane environment, which has a significant impact on the intensity forecast.
Abstract
To overcome a deficiency in the standard Ku- and Ka-band dual-wavelength radar technique, a modified version of the method is introduced. The deficiency arises from ambiguities in the estimate of the mass-weighted diameter D m of the raindrop size distribution (DSD) derived from the differential frequency ratio (DFR), defined as the difference between the radar reflectivity factors (dB) at Ku and Ka band Z Ku − Z Ka. In particular, for DFR values less than zero, there are two possible solutions of D m , leading to ambiguities in the retrieved DSD parameters. It is shown that the double solutions to D m are effectively eliminated if the DFR is modified from Z Ku − Z Ka to Z Ku − γZ Ka (dB), where γ is a constant with a value less than 0.8. An optimal radar algorithm that uses the modified DFR for the retrieval of rain and D m profiles is described. The validity and accuracy of the algorithm are tested by applying it to radar profiles that are generated from measured DSD data. Comparisons of the rain rates and D m estimated from the modified DFR algorithm to the same hydrometeor quantities computed directly from the DSD spectra (or the truth) indicate that the modified DFR-based profiling retrievals perform fairly well and are superior in accuracy and robustness to retrievals using the standard DFR.
Abstract
To overcome a deficiency in the standard Ku- and Ka-band dual-wavelength radar technique, a modified version of the method is introduced. The deficiency arises from ambiguities in the estimate of the mass-weighted diameter D m of the raindrop size distribution (DSD) derived from the differential frequency ratio (DFR), defined as the difference between the radar reflectivity factors (dB) at Ku and Ka band Z Ku − Z Ka. In particular, for DFR values less than zero, there are two possible solutions of D m , leading to ambiguities in the retrieved DSD parameters. It is shown that the double solutions to D m are effectively eliminated if the DFR is modified from Z Ku − Z Ka to Z Ku − γZ Ka (dB), where γ is a constant with a value less than 0.8. An optimal radar algorithm that uses the modified DFR for the retrieval of rain and D m profiles is described. The validity and accuracy of the algorithm are tested by applying it to radar profiles that are generated from measured DSD data. Comparisons of the rain rates and D m estimated from the modified DFR algorithm to the same hydrometeor quantities computed directly from the DSD spectra (or the truth) indicate that the modified DFR-based profiling retrievals perform fairly well and are superior in accuracy and robustness to retrievals using the standard DFR.
Abstract
Hydrological applications rely on the availability and quality of precipitation products, especially model- and satellite-based products for use in areas without ground measurements. It is known that the quality of model- and satellite-based precipitation products is complementary: model-based products exhibit high quality during cold seasons while satellite-based products are better during warm seasons. To explore the complementary behavior of the quality of the precipitation products, this study uses 2-m air temperature as auxiliary information to evaluate high-resolution (0.1°/hourly) precipitation estimates from the Weather Research and Forecasting (WRF) Model and from the version 5 Integrated Multisatellite Retrievals for GPM (IMERG) algorithm (i.e., early and final runs). The products are evaluated relative to the reference NCEP Stage IV precipitation estimates over the central United States during August 2015–July 2017. Results show that the IMERG final-run estimates are nearly unbiased, while the IMERG early-run and the WRF estimates are positively biased. The WRF estimates exhibit high correlations with the reference data when the temperature falls below 280 K. The IMERG estimates, both early and final runs, do so when the temperature exceeds 280 K. Moreover, the complementary behavior of the WRF and the IMERG products conditioned on air temperature does not vary with either season or location.
Abstract
Hydrological applications rely on the availability and quality of precipitation products, especially model- and satellite-based products for use in areas without ground measurements. It is known that the quality of model- and satellite-based precipitation products is complementary: model-based products exhibit high quality during cold seasons while satellite-based products are better during warm seasons. To explore the complementary behavior of the quality of the precipitation products, this study uses 2-m air temperature as auxiliary information to evaluate high-resolution (0.1°/hourly) precipitation estimates from the Weather Research and Forecasting (WRF) Model and from the version 5 Integrated Multisatellite Retrievals for GPM (IMERG) algorithm (i.e., early and final runs). The products are evaluated relative to the reference NCEP Stage IV precipitation estimates over the central United States during August 2015–July 2017. Results show that the IMERG final-run estimates are nearly unbiased, while the IMERG early-run and the WRF estimates are positively biased. The WRF estimates exhibit high correlations with the reference data when the temperature falls below 280 K. The IMERG estimates, both early and final runs, do so when the temperature exceeds 280 K. Moreover, the complementary behavior of the WRF and the IMERG products conditioned on air temperature does not vary with either season or location.
Abstract
Coefficients are derived for computing the polarization-corrected temperature (PCT) for 10-, 19-, 37- and 89-GHz (and similar) frequencies, with applicability to satellites in the Global Precipitation Measurement mission constellation and their predecessors. PCTs for 10- and 19-GHz frequencies have been nonexistent or seldom used in the past; developing those is the main goal of this study. For 37 and 89 GHz, other formulations of PCT have already become well established. We consider those frequencies here in order to test whether the large sample sizes that are readily available now would point to different formulations of PCT. The purpose of the PCT is to reduce the effects of surface emissivity differences in a scene and draw attention to ice scattering signals related to precipitation. In particular, our intention is to develop a PCT formula that minimizes the differences between land and water surfaces, so that signatures resulting from deep convection are not easily confused with water surfaces. The new formulations of PCT for 10- and 19-GHz measurements hold promise for identifying and investigating intense convection. Four examples are shown from relevant cases. The PCT for each frequency is effective at drawing attention to the most intense convection, and removing ambiguous signals that are related to underlying land or water surfaces. For 37 and 89 GHz, the older formulations of PCT from the literature yield generally similar values as ours, with the differences mainly being a few kelvins over oceans. An optimal formulation of PCT can depend on location and season; results are presented here separated by latitude and month.
Abstract
Coefficients are derived for computing the polarization-corrected temperature (PCT) for 10-, 19-, 37- and 89-GHz (and similar) frequencies, with applicability to satellites in the Global Precipitation Measurement mission constellation and their predecessors. PCTs for 10- and 19-GHz frequencies have been nonexistent or seldom used in the past; developing those is the main goal of this study. For 37 and 89 GHz, other formulations of PCT have already become well established. We consider those frequencies here in order to test whether the large sample sizes that are readily available now would point to different formulations of PCT. The purpose of the PCT is to reduce the effects of surface emissivity differences in a scene and draw attention to ice scattering signals related to precipitation. In particular, our intention is to develop a PCT formula that minimizes the differences between land and water surfaces, so that signatures resulting from deep convection are not easily confused with water surfaces. The new formulations of PCT for 10- and 19-GHz measurements hold promise for identifying and investigating intense convection. Four examples are shown from relevant cases. The PCT for each frequency is effective at drawing attention to the most intense convection, and removing ambiguous signals that are related to underlying land or water surfaces. For 37 and 89 GHz, the older formulations of PCT from the literature yield generally similar values as ours, with the differences mainly being a few kelvins over oceans. An optimal formulation of PCT can depend on location and season; results are presented here separated by latitude and month.
Abstract
Instantaneous surface rain rate estimates from the Global Precipitation Measurement (GPM) mission’s Dual-Frequency Precipitation Radar (DPR) and combined DPR and multifrequency microwave imager (CMB) version-5 products are compared to those from the Met Office Radarnet 4 system’s Great Britain and Ireland (GBI) radar composite product. The spaceborne and ground-based rainfall products are collocated spatially and temporally and compared at 5- and 25-km resolutions over GBI during a 3-yr period (from May 2014 to April 2017). The comparison results are evaluated as a function of both the intensity and variability of precipitation within the DPR field of view and are stratified spatially and seasonally. CMB and DPR products underestimate rain rates with respect to the Radarnet product by 21% and 31%, respectively, when considering 25-km resolution data taken within 75 km of a ground-based radar. Large variability in the discrepancies between spaceborne and ground-based rain rate estimates is the result of limitations of both systems and random errors in the collocation of their measurements. The Radarnet retrieval is affected by issues with measuring the vertical extent of precipitation at far ranges, while the GPM system struggles in properly quantifying orographic precipitation. Part of the underestimation by the GPM products appears to be a consequence of an erroneous DPR clutter identification in the presence of low freezing levels. Both products are susceptible to seasonal variations in performance and decreases in precision with increased levels of heterogeneity within the instruments’ field of view.
Abstract
Instantaneous surface rain rate estimates from the Global Precipitation Measurement (GPM) mission’s Dual-Frequency Precipitation Radar (DPR) and combined DPR and multifrequency microwave imager (CMB) version-5 products are compared to those from the Met Office Radarnet 4 system’s Great Britain and Ireland (GBI) radar composite product. The spaceborne and ground-based rainfall products are collocated spatially and temporally and compared at 5- and 25-km resolutions over GBI during a 3-yr period (from May 2014 to April 2017). The comparison results are evaluated as a function of both the intensity and variability of precipitation within the DPR field of view and are stratified spatially and seasonally. CMB and DPR products underestimate rain rates with respect to the Radarnet product by 21% and 31%, respectively, when considering 25-km resolution data taken within 75 km of a ground-based radar. Large variability in the discrepancies between spaceborne and ground-based rain rate estimates is the result of limitations of both systems and random errors in the collocation of their measurements. The Radarnet retrieval is affected by issues with measuring the vertical extent of precipitation at far ranges, while the GPM system struggles in properly quantifying orographic precipitation. Part of the underestimation by the GPM products appears to be a consequence of an erroneous DPR clutter identification in the presence of low freezing levels. Both products are susceptible to seasonal variations in performance and decreases in precision with increased levels of heterogeneity within the instruments’ field of view.
Abstract
A statistical analysis of simultaneous observations of more than 800 hailstorms over the continental United States performed by the Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) and the ground-based Next Generation Weather Radar (NEXRAD) network has been carried out. Several distinctive features of DPR measurements of hail-bearing columns, potentially exploitable by hydrometeor classification algorithms, are identified. In particular, the height and the strength of the Ka-band reflectivity peak show a strong relationship with the hail shaft area within the instrument field of view (FOV). Signatures of multiple scattering (MS) at the Ka band are observed for a range of rimed particles, including but not exclusively for hail. MS amplifies uncertainty in the effective Ka reflectivity estimate and has a negative impact on the accuracy of dual-frequency rainfall retrievals at the ground. The hydrometeor composition of convective cells presents a large inhomogeneity within the DPR FOV. Strong nonuniform beamfilling (NUBF) introduces large ambiguities in the attenuation correction at Ku and Ka bands, which additionally hamper quantitative retrievals. The effective detection of profiles affected by MS is a very challenging task, since the inhomogeneity within the DPR FOV may result in measurements that look remarkably like MS signatures. The shape of the DPR reflectivity profiles is the result of the complex interplay between the scattering properties of the different hydrometeors, NUBF, and MS effects, which significantly reduces the ability of the DPR system to detect hail at the ground.
Abstract
A statistical analysis of simultaneous observations of more than 800 hailstorms over the continental United States performed by the Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) and the ground-based Next Generation Weather Radar (NEXRAD) network has been carried out. Several distinctive features of DPR measurements of hail-bearing columns, potentially exploitable by hydrometeor classification algorithms, are identified. In particular, the height and the strength of the Ka-band reflectivity peak show a strong relationship with the hail shaft area within the instrument field of view (FOV). Signatures of multiple scattering (MS) at the Ka band are observed for a range of rimed particles, including but not exclusively for hail. MS amplifies uncertainty in the effective Ka reflectivity estimate and has a negative impact on the accuracy of dual-frequency rainfall retrievals at the ground. The hydrometeor composition of convective cells presents a large inhomogeneity within the DPR FOV. Strong nonuniform beamfilling (NUBF) introduces large ambiguities in the attenuation correction at Ku and Ka bands, which additionally hamper quantitative retrievals. The effective detection of profiles affected by MS is a very challenging task, since the inhomogeneity within the DPR FOV may result in measurements that look remarkably like MS signatures. The shape of the DPR reflectivity profiles is the result of the complex interplay between the scattering properties of the different hydrometeors, NUBF, and MS effects, which significantly reduces the ability of the DPR system to detect hail at the ground.
Resolving Surface Rain from GMI High-Frequency Channels: Limits Imposed by the Three-Dimensional Structure of PrecipitationAbstract
The scattering of microwaves at frequencies between 50 and 200 GHz by ice particles in the atmosphere is an essential element in the retrieval of instantaneous surface precipitation from spaceborne passive radiometers. This paper explores how the variable distribution of solid and liquid hydrometeors in the atmospheric column over land surfaces affects the brightness temperature (TB) measured by GMI at 89 GHz through the analysis of Dual-Frequency Precipitation Radar (DPR) reflectivity profiles along the 89-GHz beam. The objective is to refine the statistical relations between observed TBs and surface precipitation over land and to define their limits. As GMI is scanning with a 53° Earth incident angle, the observed atmospheric volume is actually not a vertical column, which may lead to very heterogeneous and seemingly inconsistent distributions of the hydrometeors inside the beam. It is found that the 89-GHz TB is mostly sensitive to the presence of ice hydrometeors several kilometers above the 0°C isotherm, up to 10 km above the 0°C isotherm for the deepest convective systems, but is a modest predictor of the surface precipitation rate. To perform a precise mapping of atmospheric ice, the altitude of the individual ice clusters must be known. Indeed, if variations in the altitude of ice are not accounted for, then the high incident angle of GMI causes a horizontal shift (parallax shift) between the estimated position of the ice clusters and their actual position. We show here that the altitude of ice clusters can be derived from the 89-GHz TB itself, allowing for correction of the parallax shift.
Abstract
The scattering of microwaves at frequencies between 50 and 200 GHz by ice particles in the atmosphere is an essential element in the retrieval of instantaneous surface precipitation from spaceborne passive radiometers. This paper explores how the variable distribution of solid and liquid hydrometeors in the atmospheric column over land surfaces affects the brightness temperature (TB) measured by GMI at 89 GHz through the analysis of Dual-Frequency Precipitation Radar (DPR) reflectivity profiles along the 89-GHz beam. The objective is to refine the statistical relations between observed TBs and surface precipitation over land and to define their limits. As GMI is scanning with a 53° Earth incident angle, the observed atmospheric volume is actually not a vertical column, which may lead to very heterogeneous and seemingly inconsistent distributions of the hydrometeors inside the beam. It is found that the 89-GHz TB is mostly sensitive to the presence of ice hydrometeors several kilometers above the 0°C isotherm, up to 10 km above the 0°C isotherm for the deepest convective systems, but is a modest predictor of the surface precipitation rate. To perform a precise mapping of atmospheric ice, the altitude of the individual ice clusters must be known. Indeed, if variations in the altitude of ice are not accounted for, then the high incident angle of GMI causes a horizontal shift (parallax shift) between the estimated position of the ice clusters and their actual position. We show here that the altitude of ice clusters can be derived from the 89-GHz TB itself, allowing for correction of the parallax shift.
Abstract
The Goddard convective–stratiform heating (CSH) algorithm, used to estimate cloud heating in support of the Tropical Rainfall Measuring Mission (TRMM), is upgraded in support of the Global Precipitation Measurement (GPM) mission. The algorithm’s lookup tables (LUTs) are revised using new and additional cloud-resolving model (CRM) simulations from the Goddard Cumulus Ensemble (GCE) model, producing smoother heating patterns that span a wider range of intensities because of the increased sampling and finer GPM product grid. Low-level stratiform cooling rates are reduced in the land LUTs for a given rain intensity because of the rain evaporation correction in the new four-class ice (4ICE) scheme. Additional criteria, namely, echo-top heights and low-level reflectivity gradients, are tested for the selection of heating profiles. Those resulting LUTs show greater and more precise variation in their depth of heating as well as a tendency for stronger cooling and heating rates when low-level dBZ values decrease toward the surface. Comparisons versus TRMM for a 3-month period show much more low-level heating in the GPM retrievals because of increased detection of shallow convection, while upper-level heating patterns remain similar. The use of echo tops and low-level reflectivity gradients greatly reduces midlevel heating from ~2 to 5 km in the mean GPM heating profile, resulting in a more top-heavy profile like TRMM versus a more bottom-heavy profile with much more midlevel heating. Integrated latent heating rates are much better balanced versus surface rainfall for the GPM retrievals using the additional selection criteria with an overall bias of +4.3%.
Abstract
The Goddard convective–stratiform heating (CSH) algorithm, used to estimate cloud heating in support of the Tropical Rainfall Measuring Mission (TRMM), is upgraded in support of the Global Precipitation Measurement (GPM) mission. The algorithm’s lookup tables (LUTs) are revised using new and additional cloud-resolving model (CRM) simulations from the Goddard Cumulus Ensemble (GCE) model, producing smoother heating patterns that span a wider range of intensities because of the increased sampling and finer GPM product grid. Low-level stratiform cooling rates are reduced in the land LUTs for a given rain intensity because of the rain evaporation correction in the new four-class ice (4ICE) scheme. Additional criteria, namely, echo-top heights and low-level reflectivity gradients, are tested for the selection of heating profiles. Those resulting LUTs show greater and more precise variation in their depth of heating as well as a tendency for stronger cooling and heating rates when low-level dBZ values decrease toward the surface. Comparisons versus TRMM for a 3-month period show much more low-level heating in the GPM retrievals because of increased detection of shallow convection, while upper-level heating patterns remain similar. The use of echo tops and low-level reflectivity gradients greatly reduces midlevel heating from ~2 to 5 km in the mean GPM heating profile, resulting in a more top-heavy profile like TRMM versus a more bottom-heavy profile with much more midlevel heating. Integrated latent heating rates are much better balanced versus surface rainfall for the GPM retrievals using the additional selection criteria with an overall bias of +4.3%.
The System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) Precipitation Observation Fusion FrameworkAbstract
Researchers now have the benefit of an unprecedented suite of space- and ground-based sensors that provide multidimensional and multiparameter precipitation information. Motivated by NASA’s Global Precipitation Measurement (GPM) mission and ground validation objectives, the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) has been developed as a unique multisensor precipitation data fusion tool to unify field observations recorded in a variety of formats and coordinate systems into a common reference frame. Through platform-specific modules, SIMBA processes data from native coordinates and resolutions only to the extent required to set them into a user-defined three-dimensional grid. At present, the system supports several ground-based scanning research radars, NWS NEXRAD radars, profiling Micro Rain Radars (MRRs), multiple disdrometers and rain gauges, soundings, the GPM Microwave Imager and Dual-Frequency Precipitation Radar on board the Core Observatory satellite, and Multi-Radar Multi-Sensor system quantitative precipitation estimates. SIMBA generates a new atmospheric column data product that contains a concomitant set of all available data from the supported platforms within the user-specified grid defining the column area in the versatile netCDF format. Key parameters for each data source are preserved as attributes. SIMBA provides a streamlined framework for initial research tasks, facilitating more efficient precipitation science. We demonstrate the utility of SIMBA for investigations, such as assessing spatial precipitation variability at subpixel scales and appraising satellite sensor algorithm representation of vertical precipitation structure for GPM Core Observatory overpass cases collected in the NASA Wallops Precipitation Science Research Facility and the GPM Olympic Mountain Experiment (OLYMPEX) ground validation field campaign in Washington State.
Abstract
Researchers now have the benefit of an unprecedented suite of space- and ground-based sensors that provide multidimensional and multiparameter precipitation information. Motivated by NASA’s Global Precipitation Measurement (GPM) mission and ground validation objectives, the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) has been developed as a unique multisensor precipitation data fusion tool to unify field observations recorded in a variety of formats and coordinate systems into a common reference frame. Through platform-specific modules, SIMBA processes data from native coordinates and resolutions only to the extent required to set them into a user-defined three-dimensional grid. At present, the system supports several ground-based scanning research radars, NWS NEXRAD radars, profiling Micro Rain Radars (MRRs), multiple disdrometers and rain gauges, soundings, the GPM Microwave Imager and Dual-Frequency Precipitation Radar on board the Core Observatory satellite, and Multi-Radar Multi-Sensor system quantitative precipitation estimates. SIMBA generates a new atmospheric column data product that contains a concomitant set of all available data from the supported platforms within the user-specified grid defining the column area in the versatile netCDF format. Key parameters for each data source are preserved as attributes. SIMBA provides a streamlined framework for initial research tasks, facilitating more efficient precipitation science. We demonstrate the utility of SIMBA for investigations, such as assessing spatial precipitation variability at subpixel scales and appraising satellite sensor algorithm representation of vertical precipitation structure for GPM Core Observatory overpass cases collected in the NASA Wallops Precipitation Science Research Facility and the GPM Olympic Mountain Experiment (OLYMPEX) ground validation field campaign in Washington State.
